A scalable space-time domain decomposition approach for solving large scale nonlinear regularized inverse ill posed problems in 4D variational data assimilation

DOI10.1007/s10915-022-01826-7zbMath1494.90048arXiv2205.06649OpenAlexW4226232069MaRDI QIDQ2148116

Emil M. Constantinescu, Luisa D'Amore, Luisa Carracciuolo

Publication date: 21 June 2022

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.06649

zbMATH Keywords

inverse problemsnonlinear least squares problemsdata assimilationscalable algorithmspace and time decomposition

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Applications of mathematical programming (90C90)

Related Items (1)

Numerical wave propagation aided by deep learning

Uses Software

• L-BFGS
• CUDA
• TAF
• ROMS
• Exshall

Cites Work

• Unnamed Item
• Toward an efficient parallel in time method for partial differential equations
• A scalable approach for variational data assimilation
• Domain decomposition and model reduction for the numerical solution of PDE constrained optimization problems with localized optimization variables
• Performance of parallel processors
• On the limited memory BFGS method for large scale optimization
• Analysis of the Turkel-Zwas scheme for the two-dimensional shallow water equations in spherical coordinates
• On the variational data assimilation problem solving and sensitivity analysis
• Efficient time domain decomposition algorithms for parabolic PDE-constrained optimization problems
• A time-parallel approach to strong-constraint four-dimensional variational data assimilation
• 50 Years of Time Parallel Time Integration
• Schwarz Methods for the Time-Parallel Solution of Parabolic Control Problems
• A decomposition‐based approach to uncertainty analysis of feed‐forward multicomponent systems
• A Domain Decomposition Approach for Uncertainty Analysis
• Recipes for adjoint code construction
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Quasi-Newton Methods, Motivation and Theory
• Algorithm 778: L-BFGS-B
• Numerical Optimization
• Towards a parallel component in a GPU–CUDA environment: a case study with the L-BFGS Harwell routine
• A non-intrusive parallel-in-time approach for simultaneous optimization with unsteady PDEs
• Approximate Gauss–Newton Methods for Nonlinear Least Squares Problems
• A method for the solution of certain non-linear problems in least squares
• Data Assimilation

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